Identification of Novel Antibacterials Using Machine Learning Techniques

Yan A. Ivanenkov, Alex A. Zhavoronkov, Renat S. Yamidanov, Ilya А. Osterman, Сергиев Петр Владимирович, Vladimir Aladinskiy, Anastasia V. Aladinskaya, Victor A. Terentiev et al.

Frontiers in Pharmacology 2019 article cited by 47

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importance 8/10 ai assisted Computational Drug Discovery Methods antibiotic discovery stage: research

Problem

Pharmaceutical companies avoid developing novel antibacterials despite urgent need for new antibiotics against resistant bacterial strains.

Why it matters

The abstract states there is an urgent need for novel antibiotics, especially against resistant bacterial strains.

What the AI did

Six in silico machine learning techniques were applied to a dataset of over 140,000 molecules with antibacterial activity against Escherichia coli to score novel molecules by antibacterial potency.

Results

not stated in source

Impact

A model was built on a large proprietary dataset, but the abstract is cut off before validation outcomes are given.

Record

Venue	Frontiers in Pharmacology
Published	2019
Work type	article
Authors	Yan A. Ivanenkov, Alex A. Zhavoronkov, Renat S. Yamidanov, Ilya А. Osterman, Сергиев Петр Владимирович, Vladimir Aladinskiy, Anastasia V. Aladinskaya, Victor A. Terentiev et al.
Citations	47 (OpenAlex, at import time)
References	53
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.3389/fphar.2019.00913
Open access	gold
Keywords	machine learning, antibacterials, drug discovery, Escherichia coli, in silico screening

Importance rating

8 / 10

major — millions of lives, an industry or a field

Abstract states urgent need for novel antibiotics against resistant strains; pharma avoidance of antibacterials is a recognized global health gap.

Basis: stated. The source itself states the scale. Stated scale: not stated in source. Method and limits.

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